Model validation and error modeling to support sequential sampling

Yao Lin, Dong Luo, Trevor Bailey, Ritesh Khire, Jiachuan Wang, Timothy W. Simpson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Several model validation and prediction error modeling techniques are studied and compared in this paper to help establish stopping criteria and identify critical regions in the design space in a sequential sampling framework. This study leads to the proposal of a two-phase sequential sampling and meta-modeling strategy, which is realized by the support of a multi-dimensional data visualization tool. These techniques have been successfully applied in the development and setup of a system-level parametric tool to support Heating, Ventilating, and Air Conditioning design. Maintaining the same level of accuracy, we observe a savings of 6-30 times the simulation effort needed for current practice. The benefits and drawbacks of the method are discussed, and opportunities are identified for future improvement.

Original languageEnglish (US)
Title of host publication2008 Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, DETC 2008
Pages1255-1264
Number of pages10
EditionPART B
StatePublished - 2009
Event2008 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, DETC 2008 - New York City, NY, United States
Duration: Aug 3 2008Aug 6 2008

Publication series

Name2008 Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, DETC 2008
NumberPART B
Volume1

Other

Other2008 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, DETC 2008
Country/TerritoryUnited States
CityNew York City, NY
Period8/3/088/6/08

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Industrial and Manufacturing Engineering
  • Mechanical Engineering

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